Hardware-in-the-loop simulation of trajectory following control for a light armoured vehicle optimised with particle swarm optimisation

Amer, Noor Hafizah; Hudha, Khisbullah; Zamzuri, Hairi; Aparow, Vimal Rau; Kadir, Zulkiffli Abd; Abidin, Amar Faiz Zainal

doi:10.1504/ijhvs.2019.101891

Cited by 13 publications

(10 citation statements)

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“…The RMS value of the lateral acceleration for HIL test was recorded as

1.5805 m / s^{2}

greater than simulation with

0.8377 m / s^{2}

. The delay and the greater RMS values for HIL test are due to the nature of the hardware parts such as friction uncertainties and the gap exists between the gears in mechanical part [42, 43] which were being neglected during designing the control system.…”

Section: Resultsmentioning

confidence: 99%

Vehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi‐speed scheduler

Wahid

Zamzuri

Amer

et al. 2020

IET intell. transp. syst

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“…The RMS value of the lateral acceleration for HIL test was recorded as

1.5805 m / s^{2}

greater than simulation with

0.8377 m / s^{2}

Section: Resultsmentioning

confidence: 99%

Vehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi‐speed scheduler

Wahid

Zamzuri

Amer

et al. 2020

IET intell. transp. syst

Self Cite

View full text Add to dashboard Cite

“…The previous work from the authors has demonstrated the derivations and verifications of this model [12,15]. Also, several previous studies have been evaluating its controller's performance on the same model [11,14,16].…”

Section: Modelling Of a 7dof Heavy Vehiclementioning

confidence: 93%

“…To ensure that the model generate response as close as possible to a real condition, rolling and air resistance are considered in this model. In terms of the steering system, the Configuration of the heavy vehicle model [12,14,15].…”

Section: Modelling Of a 7dof Heavy Vehiclementioning

confidence: 99%

“…These effects can be shown in Figure 5 where all the three variants of the Stanley controller were simulated on the heavy vehicle model with different speeds on S road. Each of the controllers was properly tuned for 6 m/s speed on the particular trajectory using particle swarm optimisation (PSO) algorithm with the same approach as presented in [13][14][15]. Table 1 shows the parameter values for each controller upon optimisations for each trajectory at 6 m/s speed, which was used throughout the testing for all speed values.…”

Section: ð1þ ð2þ ð3þmentioning

confidence: 99%

“…A metaheuristic optimisation algorithm, namely, PSO method, is used in building the knowledge database, as outlined in Amer et al [13,15] to optimise the four controller parameters for each of the v and ϕ combinations. Each of the four controller parameters is optimised for each of the v and ϕ combinations.…”

Section: Building Knowledge Database Using Particle Swarm Optimisationmentioning

confidence: 99%

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Knowledge-Based Controller Optimised with Particle Swarm Optimisation for Adaptive Path Tracking Control of an Autonomous Heavy Vehicle

Amer¹,

Hudha²,

Zamzuri³

et al. 2021

Automation and Control

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This chapter discusses the development of an adaptive path tracking controller equipped with a knowledge-based supervisory algorithm for an autonomous heavy vehicle. The controller was developed based on a geometric/kinematic controller, the Stanley controller. One of the mostly known issues with any geometric/kinematic controller is that a properly tuned controller may not be valid in a different operating region than the one it was being tuned/optimised on. Therefore, this study proposes an adaptive algorithm to automatically choose an optimal controller parameter depending on the manoeuvring and vehicle conditions. An optimal knowledge database is developed for an adaptive algorithm to automatically obtain the parameter values based on the vehicle speed, v, and heading error, ϕ. Several simulations are carried out with different trajectories and speeds to evaluate the effectiveness of the controller against its predecessors, namely, Stanley and the nonadaptive modified Stanley (Mod St) controllers. The simulated steering actions are then compared against human driver's experimental data along the predefined paths. It was shown that the proposed adaptive algorithm managed to guide the heavy vehicle successfully and adapt to various trajectories with different vehicle speeds while recording lateral error improvement of up to 82% compared to the original Stanley controller.

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Path following control of tracked vehicle using modified sup controller optimized with particle swarm optimization (PSO)

Subari

Hudha

Kadir

et al. 2022

Int. J. Dynam. Control

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Hardware-in-the-loop simulation of trajectory following control for a light armoured vehicle optimised with particle swarm optimisation

Cited by 13 publications

References 0 publications

Vehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi‐speed scheduler

Vehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi‐speed scheduler

Knowledge-Based Controller Optimised with Particle Swarm Optimisation for Adaptive Path Tracking Control of an Autonomous Heavy Vehicle

Path following control of tracked vehicle using modified sup controller optimized with particle swarm optimization (PSO)

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